Welcome to SUSMAT

The SUSMAT university profiling measure enhances the design of sustainable materials and manufacturing with data-driven approach to solve global energy, health and well-being, and industrial production challenges.

SUSMAT brings together engineering, natural sciences and medical biomaterial science with data digitalisation and artificial intelligence, supporting cross-disciplinary research in the University’s three profiling areas that focus on immune mediated diseases, the evolution of human diversity, and the design of sustainable materials.

Themes

Diagram showing information flows between SUSMAT themes

Energy

Data-driven development of materials and devices to enable green transition will be significantly accelerated. This, combined with the knowledge of material chemistry and physics in fabrication, functionalisation and characterisation provides rational and tuneable material design for different energy applications such as batteries, solar cells, super capacitors, OLEDs/LEDs and electrocatalysts.

Health

Future healthcare challenges require novel biomaterials and multifunctional delivery vehicles. A rational material design with a combined expertise of organic and inorganic material sciences is needed. A multidisciplinary technology platform will be developed for manufacturing novel drug delivery vehicles, related to targeted extrahepatic delivery, robust sustainable manufacturing, and stable formulation of biological drugs.

Manufacturing

Industry 4.0 is the drive towards rapid change to technology, industries, and manufacturing due to increasing interconnectivity and smart automation, taking advantage of digital manufacturing and digital design (simulations), creative additive manufacturing utilising innovative alloys and coaxial printing. Sustainable production of chemicals and fuels utilising microbiological methods, renewable energy, etc. This approach requires optimisation of re-x (-use, -pair, -furbishing, -cycling).

Data-driven materials science

Emergence of materials science data repositories has ushered in the era of data-driven materials science, but applications to experimental data are stalling. Here, data digitialisation strategies for organising data collection with electronic notebooks, metadata encoding, dataset curation and quality checks are essential. Based on the collected experimental data, we can employ AI algorithms to infer synthesis-property-function relationships and accelerate the discovery of new (sustainable) material compounds, processes and devices.